Robust OFDM Modem for a Swarm of Underwater Autonomous Vehicles

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The Pentose Phosphate Pathway Regulates the Circadian Clock

The circadian clock is a ubiquitous timekeeping system that organizes the behavior and physiology of organisms over the day and night. Current models rely on transcriptional networks that coordinate circadian gene expression of thousands of transcripts. However, recent studies have uncovered phylogenetically conserved redox rhythms that can occur independently of transcriptional cycles. Here we identify the pentose phosphate pathway (PPP), a critical source of the redox cofactor NADPH, as an important regulator of redox and transcriptional oscillations. Our results show that genetic and pharmacological inhibition of the PPP prolongs the period of circadian rhythms in human cells, mouse tissues, and fruit flies. These metabolic manipulations also cause a remodeling of circadian gene expression programs that involves the circadian transcription factors BMAL1 and CLOCK, and the redox-sensitive transcription factor NRF2. Thus, the PPP regulates circadian rhythms via NADPH metabolism, suggesting a pivotal role for NADPH availability in circadian timekeeping.Peer reviewe

On the spontaneous stochastic dynamics of a single gene: complexity of the molecular interplay at the promoter

International audienceBACKGROUND: Gene promoters can be in various epigenetic states and undergo interactions with many molecules in a highly transient, probabilistic and combinatorial way, resulting in a complex global dynamics as observed experimentally. However, models of stochastic gene expression commonly consider promoter activity as a two-state on/off system. We consider here a model of single-gene stochastic expression that can represent arbitrary prokaryotic or eukaryotic promoters, based on the combinatorial interplay between molecules and epigenetic factors, including energy-dependent remodeling and enzymatic activities. RESULTS: We show that, considering the mere molecular interplay at the promoter, a single-gene can demonstrate an elaborate spontaneous stochastic activity (eg. multi-periodic multi-relaxation dynamics), similar to what is known to occur at the gene-network level. Characterizing this generic model with indicators of dynamic and steady-state properties (including power spectra and distributions), we reveal the potential activity of any promoter and its influence on gene expression. In particular, we can reproduce, based on biologically relevant mechanisms, the strongly periodic patterns of promoter occupancy by transcription factors (TF) and chromatin remodeling as observed experimentally on eukaryotic promoters. Moreover, we link several of its characteristics to properties of the underlying biochemical system. The model can also be used to identify behaviors of interest (eg. stochasticity induced by high TF concentration) on minimal systems and to test their relevance in larger and more realistic systems. We finally show that TF concentrations can regulate many aspects of the stochastic activity with a considerable flexibility and complexity. CONCLUSIONS: This tight promoter-mediated control of stochasticity may constitute a powerful asset for the cell. Remarkably, a strongly periodic activity that demonstrates a complex TF concentration-dependent control is obtained when molecular interactions have typical characteristics observed on eukaryotic promoters (high mobility, functional redundancy, many alternate states/pathways). We also show that this regime results in a direct and indirect energetic cost. Finally, this model can constitute a framework for unifying various experimental approaches. Collectively, our results show that a gene - the basic building block of complex regulatory networks - can itself demonstrate a significantly complex behavior

Helios Is Associated with CD4 T Cells Differentiating to T Helper 2 and Follicular Helper T Cells In Vivo Independently of Foxp3 Expression

Although in vitro IL-4 directs CD4 T cells to produce T helper 2 (Th2)-cytokines, these cytokines can be induced in vivo in the absence of IL-4-signalling. Thus, mechanism(s), different from the in vitro pathway for Th2-induction, contribute to in vivo Th2-differentiation. The pathway for in vivo IL-4-independent Th2-differentiation has yet to be characterized. - upregulate Th1 features - T-bet and IFN-γ - but not Helios. In addition, CD4 T cells induced to produce Th2 cytokines in vitro do not express Helios. The kinetics of Helios mRNA and protein induction mirrors that of GATA-3. The induction of IL-4, IL-13 and CXCR5 by alumOVA requires NF-κB1 and this is also needed for Helios upregulation. Importantly, Helios is induced in Th2 and TFh cells without parallel upregulation of Foxp3. These findings suggested a key role for Helios in Th2 and TFh development in response to alum-protein vaccines. We tested this possibility using Helios-deficient OTII cells and found this deficiency had no discernable impact on Th2 and TFh differentiation in response to alumOVA.Helios is selectively upregulated in CD4 T cells during Th2 and TFh responses to alum-protein vaccines in vivo, but the functional significance of this upregulation remains uncertain

Contributions aux statistiques et méthodes d’apprentissage automatique pour la détection d’attaques basée sur la physique dans les systèmes industriels

The objective of this thesis is the development of new cyberattack detection methods based on machine learning techniques. This work focused on industrial systems, and consisted in characterizing the normal behavior of the physical signals of the system. This characterization then allows us to detect anomalies that occur in the system, these anomalies being deviations from the learned nominal behavior. The work presented here is based on the RDT (Random Distortion Testing) theory, which stems from statistical decision theory, and offers an optimal test to determine whether some phenomenon lies close enough to some model, without requiring any knowledge about its probability distribution. We used this theory as a basis to develop a changedetection method, which was then successfully applied to real signals, while also allowing control of the false-alarm rate. We developed an extension of the RDT framework to account for the estimation of the model and of the noise variance, which were assumed to be known in the initial theory. This changedetection method has then been used as a basis to characterize the different phases of the signals in the system using clustering methods. Our first attempts to develop a complete learning and anomaly-detection method have yielded encouraging results for attack detection on real signals.L’objectif de cette thèse est de développer de nouvelles méthodes de détection de cyberattaques basées sur des techniques d’apprentissage automatique. Ces travaux se sont concentrés sur l’étude de systèmes industriels, et plus spécifiquement la caractérisation du comportement normal des signaux physiques du système. Cette caractérisation permet ensuite de détecter des anomalies du système comme étant des déviations du comportement du système par rapport à ce modèle nominal appris. Les travaux effectués sont basés sur la théorie RDT (Random Distortion Testing), issue de la théorique statistique de la décision et permettant de donner un test optimal pour déterminer si une grandeur est suffisamment proche ou non d’un modèle donné, sans en connaître la distribution de probabilité. Cette théorie a été utilisée comme base afin de développer une méthode de détection de changement et a été appliquée avec succès sur des signaux réels, permettant également de contrôler le taux de fausses alarmes. Une extension de la théorie RDT a été développée afin de prendre en compte l’estimation du modèle et de la variance du bruit, supposés connus dans la théorie initiale. La méthode de détection de changements développée a ensuite été utilisée comme base pour caractériser les différentes phases des signaux du système via des méthodes de clustering. Les premiers essais d’une méthode complète d’apprentissage et de détection d’anomalies effectués sur des signaux réels offrent des résultats encourageants pour la détection d’attaques

Asymptotic Random Distortion Testing for Anomaly Detection

International audienceIn connection with cybersecurity issues in ICS, we consider the problem of detecting yet unknown attacks by presenting a theoretical framework for the detection of anomalies when the observations have unknown distributions. We illustrate the relevance of this framework with experimental results

Frame Detection and Synchronization for a SIMO Receiver in presence of Doppler Shift for Underwater Acoustic Communications

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Synchronisation trame dans un contexte multicapteurs pour communication acoustique sous-marine

International audienceIn underwater acoustics communications, Doppler shift heavily impacts the receiver performance. Indeed the signal is received compressed or dilated making it hard to detect and synchronize the signal. Therefore, it is mandatory to estimate the Doppler shift and compensate it. In this article, a new multi-sensors method is proposed to jointly estimate the Doppler shift, detect and synchronize the signal.En communications acoustiques sous-marine, l’effet Doppler affecte lourdement la transmission. La compression/dilatation du signalqu’il cause rend la détection et synchronisation du signal difficiles. Afin de correctement récupérer le signal, il est primordial d’estimer le Dopplersubi et de le compenser. Dans cet article, une nouvelle méthode est proposée pour réaliser conjointement l’estimation du Doppler, la détection et synchronisation du signal dans le cadre d’une réception multicapteurs en présence de trajets multiples

Asymptotic Random Distortion Testing and Application to Change-in-Mean Detection

International audienceWe introduce an extension of the Random Distortion Testing (RDT) framework which allows its use when the noise variance is estimated. This asymptotic extension, named AsympRDT, shows that we asymptotically retain the level of the RDT test as the estimate of the noise variance converges to its real value. The validity of this approach is justified through both theoretical and simulation results. We make use of AsympRDT to develop a change-in-mean detection method for time series. It features three parameters: the size of the processed blocks, the maximum desired false alarm rate and a tolerance. We then show a use-case for this method in cybersecurity for Industrial Control Systems (ICS) as part of an anomaly and cyberattack detection system, where it can be used for segmenting signals and learning normal behaviors